Can an AI wrapper qualify for SR&ED?

It depends.

The SR&ED program is designed to reward Canadian companies that overcome technological uncertainties through systematic investigation and achieve measurable advancements. Whether your AI wrapper project qualifies comes down to the substance of the work, not the buzzwords around it.

When it is unlikely to qualify

If your wrapper simply combines existing APIs, adds a user interface, or packages standard functions into a convenient form, it will not meet SR&ED’s requirements. Using well-documented libraries or SDKs in the way they were intended rarely involves technological uncertainty or advancement.

When it may qualify

An AI wrapper can be eligible if it addresses a challenge that does not have a standard, documented solution, and if the team works through that challenge in a structured way. Examples include:

• Achieving significant performance gains under severe hardware constraints
• Creating new orchestration methods for multiple AI models to improve accuracy or reduce latency
• Developing prompt engineering or fine-tuning techniques that are repeatable and measurable
• Integrating AI components in novel ways to achieve functionality not previously possible through off-the-shelf tools

Meeting the three-part test

To qualify, you must demonstrate:

1. Technological uncertainty: There was no known way to achieve your goal at the start.
2. Systematic investigation: You formed hypotheses, ran controlled experiments, and documented your results.
3. Technological advancement: You generated new knowledge that could be applied beyond your specific implementation.

The biggest pitfall

Even if the technical work qualifies in theory, many companies fail because they cannot prove it. Without contemporaneous documentation — clear, time-stamped records of challenges, experiments, and results — the CRA will likely reject the claim.

Quick checklist: Could your AI wrapper potentially qualify?

You may have a stronger case if you can answer “yes” to most of the following:

• Are you solving a technical problem with no standard, publicly available solution?
• Did you have to experiment with multiple design approaches to achieve the desired result?
• Are you working with novel orchestration, fine-tuning, or optimization techniques beyond basic API calls?
• Have you documented each hypothesis, experiment setup, and result in real time?
• Did the project result in a repeatable method or architecture that did not exist before?
• Can your work be shown to advance performance, accuracy, scalability, or functionality beyond known benchmarks?

If you answer “no” to most of these questions, the project may not meet the SR&ED criteria.

Example: AI wrapper that could qualify

A team builds a wrapper that connects multiple large language models (LLMs) and a custom rules-based post-processing layer to generate medical device compliance reports.

The challenge is that no existing tool can reliably meet both the accuracy and formatting requirements under EU MDR and FDA 21 CFR Part 11 simultaneously. The team experiments with:

• Dynamic model selection based on input complexity
• Custom fine-tuning of domain-specific terminology recognition
• Hybrid inference pipelines combining statistical QA checks with transformer outputs
• Optimized prompt orchestration that reduces latency while preserving accuracy

Each iteration is documented: objectives, test setup, measured error rates, and how each change impacts performance. By the end, they have a reproducible method that consistently meets regulatory accuracy thresholds in under 1.5 seconds per request, something not achievable with standard API calls alone.

This meets the technological uncertainty, systematic investigation, and technological advancement criteria.

Example: AI wrapper that would likely not qualify

A developer creates a wrapper around an LLM API to:

• Accept plain text input from a web form
• Send it directly to an API like GPT-4 with minimal prompt templating
• Display the returned text in a formatted PDF

The wrapper improves usability for end users but does not introduce novel algorithms, new performance characteristics, or technical problem-solving beyond standard SDK usage. All components are implemented using documented methods without the need to experiment or overcome unknowns.

There is no technological uncertainty because the integration steps and expected results are well understood. There is no systematic investigation or advancement because no new technical knowledge is generated.

If you are building an AI wrapper and want to know where your project stands, SREDSimplify’s free pre-screener will walk you through the eligibility questions and give you a quick, evidence-based assessment.

You can try it here: https://sredsimplify.com/

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